| dc.description.abstract | Recently, the importance of few shot learning field has obviously increased, and variety of famous learning methods, like Meta-learning and Continuous learning. These methods proposed to solve few shot learning, which main purpose is both training model with only few amounts of data and maintaining high generalization ability. MAML, which is an elegant and effective Meta-Learning method demonstrates its powerful performance in Omniglot and Mini-Imagenet N-way K-shot classification experiments. However, the recent research points out that the problems of instable performance of MAML and others model′s architecture problems. On the other hand, continuous learning models usually face the issue of catastrophic forgetting when the models not only learn new tasks but keep remembering the knowledge about previous tasks. Therefore, we propose our method, En-MAML, which is based on MAML framework, to combine the flexible adaptation characteristic from meta-learning with the stability performance from continual learning. We evaluate our model on Omniglot and Mini-Imagenet datasets, and follow the N-way K-shot experiment protocol. From our experiment results, our model demonstrates higher accuracy and stability on Omniglot and Mini-Imagenet. | en_US |